Academic literature on the topic 'Artificial orthogonalization'
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Journal articles on the topic "Artificial orthogonalization"
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Robinson, P. John, and A. Saranraj. "Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems." Indian Journal Of Science And Technology 17, no. 24 (2024): 2529–37. http://dx.doi.org/10.17485/ijst/v17i24.1386.
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Objectives: To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). Methods: The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is improvised. Findings: The proposed Gram-Scmidt Orthogonalization process is utilized in Intuitionistic Fuzzy Artificial Neural Network model. The Delta learning rule is utilized in the process of the Neural Network, where the Intuitionistic Fuzzy nature of the input data is transformed into a fuzzy data and then the ranking of the alternatives is done based on the weights updation through the learning phase of the ANN. Once the vector is trained out of the learning phase, it is then processed through the activation function for the final selection of the best alternative required of the Multiple Attribute Group Decision Making (MAGDM) problem posed in this work. To demonstrate the usefulness and applicability of this new method with the Gram-Schmidt process, the numerical example also adds more insight to the proposed methodology of ANN with the application of some Linear Space techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network model are based on learning rules or using some other calculations. The proposed Gram-Schmidt Orthogonalization process is used to find the orthogonal basis that are used as input training vectors in the Delta learning rule for ANN. Keywords: MAGDM, ANN, Aggregation operators, Learning Rules, Intuitionistic Fuzzy sets, Gram-Scmidt Orthogonalization
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P, John Robinson, and Saranraj A. "Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems." Indian Journal of Science and Technology 17, no. 24 (2024): 2529–37. https://doi.org/10.17485/IJST/v17i24.1386.
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Abstract <strong>Objectives:</strong> To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). <strong>Methods:</strong> The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is improvised. <strong>Findings:</strong> The proposed Gram-Scmidt Orthogonalization process is utilized in Intuitionistic Fuzzy Artificial Neural Network model. The Delta learning rule is utilized in the process of the Neural Network, where the Intuitionistic Fuzzy nature of the input data is transformed into a fuzzy data and then the ranking of the alternatives is done based on the weights updation through the learning phase of the ANN. Once the vector is trained out of the learning phase, it is then processed through the activation function for the final selection of the best alternative required of the Multiple Attribute Group Decision Making (MAGDM) problem posed in this work. To demonstrate the usefulness and applicability of this new method with the Gram-Schmidt process, the numerical example also adds more insight to the proposed methodology of ANN with the application of some Linear Space techniques.<strong> Novelty:</strong> Most of the research done on Intuitionistic Fuzzy Artificial Neural Network model are based on learning rules or using some other calculations. The proposed Gram-Schmidt Orthogonalization process is used to find the orthogonal basis that are used as input training vectors in the Delta learning rule for ANN. <strong>Keywords:</strong> MAGDM, ANN, Aggregation operators, Learning Rules, Intuitionistic Fuzzy sets, Gram-Scmidt Orthogonalization
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Bezsonov, O. O., K. O. Oliinyk, O. S. Romanyk, O. G. Rudenko, and N. M. Serdiuk. "Factorized Perseptron Training Algorithms In The Problem Of Constructing A Nonlinear Model." Bionics of Intelligence 1, no. 94 (2020): 23–29. http://dx.doi.org/10.30837/bi.2020.1(94).04.
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In order to improve the computational properties of training procedures for artificial neural networks (ANNs),which, being universal approximators, make it possible to restore any arbitrarily complex continuous nonlinear functionwith a given accuracy, their factorized forms have been developed based on the Cholesky, Householder transformationsand Gram-Schmidt orthogonalization. The analysis of their properties showed that the most effective way to increasethe stability of the learning algorithm is to use the Householder transformation
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Dmitriy, Domin. "ARTIFICIAL ORTHOGONALIZATION IN SEARCHING OF OPTIMAL CONTROL OF TECHNOLOGICAL PROCESSES UNDER UNCERTAINTY CONDITIONS." Eastern-European Journal of Enterprise Technologies 5, no. 9 (65) (2013): 45–53. https://doi.org/10.5281/zenodo.3602372.
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The paper describes further development of the methods of artificial orthogonalization of passive experiment designs describing the experimental values of the output function in a multi-dimensional factor space of a small sample of fuzzy data. This allows fuzzy clustering for the formation of subspaces and further local description of the response function; building local regression equations in the subspaces of full factorial space; calculating the values of the response function at the points of the orthogonalized design of experiment. The methods for processing the asymmetrical design of factorial experiment based on the use of procedure of forming a truncated orthogonal response design for elimination of insignificant factors and interactions in a small sample of fuzzy data, allowing the formation of the orthogonal design to calculate the coefficients of the regression equation, which describes the output parameters of the system in the space of fuzzy values of input variables; ability to select the most representative designs that minimize the maximum estimate of the variance of the output variable; obtaining of fuzzy data of adequate mathematical models on the multifactor space, relating the components of the output parameters of the system and parameters used in the description of the states of the system, are described. The example of the application of the proposed methods of artificial orthogonalization for solving scientific and practical problems of creating the methodology for determining the structure and parameters of the models describing the processes of electrosmelting under uncertainty conditions, which allows finding the optimal technological process control in terms of the end state, is shown.
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Domin, Dmitriy. "Artificial orthogonalization in searching of optimal control of technological processes under uncertainty conditions." Eastern-European Journal of Enterprise Technologies 5, no. 9(65) (2013): 45–53. http://dx.doi.org/10.15587/1729-4061.2013.18452.
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Oku, Makito, Takaki Makino, and Kazuyuki Aihara. "Pseudo-Orthogonalization of Memory Patterns for Associative Memory." IEEE Transactions on Neural Networks and Learning Systems 24, no. 11 (2013): 1877–87. http://dx.doi.org/10.1109/tnnls.2013.2268542.
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Zeng, Guoqiang, Baihai Zhang, Fenxi Yao, and Senchun Chai. "Modified bidirectional extreme learning machine with Gram–Schmidt orthogonalization method." Neurocomputing 316 (November 2018): 405–14. http://dx.doi.org/10.1016/j.neucom.2018.08.029.
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D., Demin. "Application of artificial orthogonalization in search for optimal control of technological processes under uncertainty." Eastern-European Journal of Enterprise Technologies 5, no. 9 (65) (2013): 45–53. https://doi.org/10.15587/1729-4061.2013.18452.
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The aim of research is to develop a methodology for determining the structure and parameters of models that describe technological processes in conditions of uncertainty, which allows finding optimal control at all the main stages of such processes. The object of research is a technological process that can be described by a mathematical model of the "composition - properties" type, which determines the quality of the product at the output of such a process. In this case, the technological process was considered as a generalized concept, a characteristic feature of which is the generality of the version of the mathematical description by a model of this type. The methods of experiment planning and fuzzy mathematics were chosen as research methods. The technology of artificial orthogonalization is proposed, which makes it possible to construct mathematical models of technological processes used to search for their optimal control under conditions of uncertainty. It is shown that an effective way to overcome the main problem of using classical methods to find the optimal control of complex technological processes, due to the impossibility of measuring the parameters describing the process, is to construct regression equations that adequately relate the output variables – the essence of the product quality parameters, and the input variables, which are fuzzy numbers and also an analysis of the response surface they describe. It has been theoretically and experimentally proved that the developed technology of artificial orthogonalization makes it possible to optimally estimate the parameters of models describing technological processes in conditions when the input variables are fuzzy numbers, and the data sample for determining the structure and estimating the parameters of models is small. This makes it possible to build an adequate analytical description of these processes based on a small sample of data available for control in production conditions during normal operation of industrial equipment. The obtained results can be used to obtain adequate mathematical models and find the optimal control in terms of the final state of complex technological processes under uncertainty.
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HAGIWARA, Katsuyuki. "Nonparametric Regression Method Based on Orthogonalization and Thresholding." IEICE Transactions on Information and Systems E94-D, no. 8 (2011): 1610–19. http://dx.doi.org/10.1587/transinf.e94.d.1610.
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Sánchez-Sánchez, Pablo, and Marco A. Arteaga-Pérez. "Improving force tracking control performance in cooperative robots." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141770896. http://dx.doi.org/10.1177/1729881417708969.
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In order to obtain the environment’s information, cooperative robots could need a lot of sensors. A possible solution to reduce the number of sensors might be the use of control–observer structures. In this article, we have designed a control algorithm by using a modified hybrid computed torque method based on the principle of orthogonalization, but in order to avoid the use of tachometers in the implementation, we are including a velocity observer. The stability proof is developed by using the theory of Lyapunov. Simulation of the proposed control structure compared with a well-known control structure via the performance index analysis is presented. Experimental tests are implemented with the control structure that has the best performance index.
Dissertations / Theses on the topic "Artificial orthogonalization"
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Димко, Єгор Павлович. "Моделі та методи оптимального керування індукційним дуплекс-процесом за умов невизначеності". Thesis, Національний технічний університет "Харківський політехнічний інститут", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38635.
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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018. Дисертація присвячена вирішенню актуальної науково-практичної задачі – розробці методів оптимального управління в умовах невизначеності. Показана можливість побудови адекватної математичної моделі індукційного дуплекс-процесу плавки як об'єкта управління в умовах неможливості реалізації плану активного експерименту в виробничих умовах. На основі цього запропоновано для опису кінцевого стану в задачі пошуку оптимального за кінцевим станом управління використовувати результати параметричного опису за визначенням локально-оптимальних значень вхідних змінних на основі реалізації процедури рідж-аналізу. Показано, як з використанням комбінованої процедури штучної ортогоналізації за даними пасивного експерименту при довільній формі плану експерименту і центрального ортогонального планування отримати таке параметричне опис. Розв'язана задача синтезу оптимального управління індукційної плавкою в печах ІСТ1 / 0.8-М5 в умовах альтернативних стратегій і доведено, що при виборі стратегії плавлення на "болоті" фазова траєкторія буде постійно змінюватися внаслідок корекції початкового стану, що обумовлено зміною швидкості розплавлення при обраному способі управління. Показано, як оптимальне за швидкодією управління може бути отримано з використанням принципу максимуму Понтрягіна в умовах обліку невизначеності в описі початкового стану об'єкта управління. Синтезований оптимальний регулятор температурного режиму в індукційної міксері на основі мультіальтернатівного опису кінцевого стану, характерною особливістю якого є використання оптимальних рішень рідж-аналізу і параметричної класифікації температурного режиму. Показано, що такий підхід може бути застосований для блоку логічних умов при логічному синтезі комбінованої системи управління індукційним дуплекс-процесом.<br>Thesis for the degree of candidate of technical sciences in specialty 05.13.03 – systems and control processes. – National Technical University "Kharkov Polytechnic Institute", Kharkov, 2018. The thesis is devoted to the solution of an actual scientific and practical problem – the development of optimal control methods in conditions of uncertainty. The possibility of building an adequate mathematical model of an induction duplex melting process as a control object under the conditions of impossibility of implementing an active experiment plan under production conditions is shown. Based on this, it is proposed to use the results of the parametric description by definition of the local-optimal values of the input variables based on the implementation of the ridge analysis procedure to describe the final state in the problem of finding the optimal by the final state control. It is shown how using a combined procedure of artificial orthogonalization according to a passive experiment with an arbitrary form of the experiment plan and central orthogonal planning to obtain such a parametric description. The problem of synthesizing optimal control of induction melting in IST1 / 0.8-M5 furnaces in terms of alternative strategies was solved and it was proved that when choosing a melting strategy in the “bog” phase trajectory will constantly change due to the correction of the initial state, which is caused by the change in melting rate with the selected control method. It is shown how the optimal in terms of speed control can be obtained using the Pontryagin maximum principle in terms of taking into account the uncertainty in the description of the initial state of the control object. An optimal temperature regulator was synthesized in an induction mixer based on a multi-alternative description of the final state, a characteristic feature of which is the use of optimal solutions of ridge analysis and parametric classification of the temperature regime. It is shown how such an approach can be applied to a block of logical conditions in the logical synthesis of a combined control system of an induction duplex process.
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Димко, Єгор Павлович. "Моделі та методи оптимального керування індукційним дуплекс-процесом за умов невизначеності". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38290.
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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018.
Дисертація присвячена вирішенню актуальної науково-практичної задачі – розробці методів оптимального управління в умовах невизначеності. Показана можливість побудови адекватної математичної моделі індукційного дуплекс-процесу плавки як об'єкта управління в умовах неможливості реалізації плану активного експерименту в виробничих умовах. На основі цього запропоновано для опису кінцевого стану в задачі пошуку оптимального за кінцевим станом управління використовувати результати параметричного опису за визначенням локально-оптимальних значень вхідних змінних на основі реалізації процедури рідж-аналізу. Показано, як з використанням комбінованої процедури штучної ортогоналізації за даними пасивного експерименту при довільній формі плану експерименту і центрального ортогонального планування отримати таке параметричне опис. Розв'язана задача синтезу оптимального управління індукційної плавкою в печах ІСТ1 / 0.8-М5 в умовах альтернативних стратегій і доведено, що при виборі стратегії плавлення на "болоті" фазова траєкторія буде постійно змінюватися внаслідок корекції початкового стану, що обумовлено зміною швидкості розплавлення при обраному способі управління. Показано, як оптимальне за швидкодією управління може бути отримано з використанням принципу максимуму Понтрягіна в умовах обліку невизначеності в описі початкового стану об'єкта управління. Синтезований оптимальний регулятор температурного режиму в індукційної міксері на основі мультіальтернатівного опису кінцевого стану, характерною особливістю якого є використання оптимальних рішень рідж-аналізу і параметричної класифікації температурного режиму. Показано, що такий підхід може бути застосований для блоку логічних умов при логічному синтезі комбінованої системи управління індукційним дуплекс-процесом.<br>Thesis for the degree of candidate of technical sciences in specialty 05.13.03 – systems and control processes. – National Technical University "Kharkov Polytechnic Institute", Kharkov, 2018. The thesis is devoted to the solution of an actual scientific and practical problem – the development of optimal control methods in conditions of uncertainty. The possibility of building an adequate mathematical model of an induction duplex melting process as a control object under the conditions of impossibility of implementing an active experiment plan under production conditions is shown. Based on this, it is proposed to use the results of the parametric description by definition of the local-optimal values of the input variables based on the implementation of the ridge analysis procedure to describe the final state in the problem of finding the optimal by the final state control. It is shown how using a combined procedure of artificial orthogonalization according to a passive experiment with an arbitrary form of the experiment plan and central orthogonal planning to obtain such a parametric description. The problem of synthesizing optimal control of induction melting in IST1 / 0.8-M5 furnaces in terms of alternative strategies was solved and it was proved that when choosing a melting strategy in the "bog" phase trajectory will constantly change due to the correction of the initial state, which is caused by the change in melting rate with the selected control method. It is shown how the optimal in terms of speed control can be obtained using the Pontryagin maximum principle in terms of taking into account the uncertainty in the description of the initial state of the control object. An optimal temperature regulator was synthesized in an induction mixer based on a multi-alternative description of the final state, a characteristic feature of which is the use of optimal solutions of ridge analysis and parametric classification of the temperature regime. It is shown how such an approach can be applied to a block of logical conditions in the logical synthesis of a combined control system of an induction duplex process.
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(8740647), William Steven Singleton. "Increasing CNN Representational Power Using Absolute Cosine Value Regularization." Thesis, 2020.
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The Convolutional Neural Network (CNN) is a mathematical model designed to distill input information into a more useful representation. This distillation process removes information over time through a series of dimensionality reductions, which ultimately, grant the model the ability to resist noise, and generalize effectively. However, CNNs often contain elements that are ineffective at contributing towards useful representations. This Thesis aims at providing a remedy for this problem by introducing Absolute Cosine Value Regularization (ACVR). This is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in R<sup>n</sup>. This method should in theory, lead to a more effective balance between information loss and representational power, ultimately, increasing network performance. The following Thesis proposes and examines the mathematics and intuition behind ACVR, and goes on to propose Dynamic-ACVR (D-ACVR). This Thesis also proposes and examines the effects of ACVR on the filters of a low-dimensional CNN, as well as the effects of ACVR and D-ACVR on traditional Convolutional filters in VGG-19. Finally, this Thesis proposes and examines regularization of the Pointwise filters in MobileNetv1.
Book chapters on the topic "Artificial orthogonalization"
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Li, Xuan, Kaicheng Li, Xiangui Xiao, and Beiao Li. "A VMD Harmonic Detection Method Based on Improved SVD Denoising." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231216.
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Harmonic detection is a vital problem in power system, but the accuracy of harmonic detection is affected by noise. In order to suppress noise, this paper proposes the improved singular value decomposition (SVD) to denoise. Then, the optimal number of decomposition modes of variational mode decomposition (VMD) is determined with the aim of minimizing the residual of signal energy after Schmidt orthogonalization. The amplitude and frequency of each harmonic are detected by the Hilbert transform. The simulation shows that the algorithm can accurately identify the information characteristics of each harmonic and inter-harmonic, and has the advantages of high accuracy and small error.
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Khan, Ahmad Saeed, Erik Schaffernicht, and Johannes Andreas Stork. "On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240818.
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Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant variables. We demonstrate in experiments that this leads to prediction errors. We disentangle pre-treatment variables with a deep embedding method and explicitly identify and represent irrelevant variables, additionally to instrumental, confounding and adjustment latent factors. To this end, we introduce a reconstruction objective and create an embedding space for irrelevant variables using an attached autoencoder. Instead of relying on serendipitous suppression of irrelevant variables as in previous deep disentanglement approaches, we explicitly force irrelevant variables into this embedding space and employ orthogonalization to prevent irrelevant information from leaking into the latent space representations of the other factors. Our experiments with synthetic and real-world benchmark datasets show that we can better identify irrelevant variables and more precisely predict treatment effects than previous methods, while prediction quality degrades less when additional irrelevant variables are introduced.
Conference papers on the topic "Artificial orthogonalization"
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Rose, Adam I., Bindu Chandna, and Kevin B. Burke. "Phase-Only Artificial Noise for Secure Communication with Multiple Antennas Using Simultaneous Orthogonalization." In 2022 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2022. http://dx.doi.org/10.1109/wifs55849.2022.9975471.
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Mohamed, M. M. A., and B. Far. "A Fast Technique for White Blood Cells Nuclei Automatic Segmentation Based on Gram-Schmidt Orthogonalization." In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.133.
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Dall'Aqua, Marcelo J., Emilio J. R. Coutinho, Eduardo Gildin, Zhenyu Guo, Hardik Zalavadia, and Sathish Sankaran. "Input-Output Invariant Fast Proxy Models for Production Optimization." In SPE Latin American and Caribbean Petroleum Engineering Conference. SPE, 2023. http://dx.doi.org/10.2118/213117-ms.
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Abstract This work aims to obtain reduced-order models for fluid flows in porous media that can be used for optimal well-control design and are they are equipped with input-output tracking capabilities. Meeting the net-zero emission paradigm will require a realignment of hydrocarbon production strategies with other forms of energy production, such as hydrogen and geothermal. Profiting from all these energy sources is only possible if accurate and timely predictions of the injection-production behavior of fluids, including geomechanics issues in the subsurface, can be attained. High-fidelity reservoir simulation provides accurate characterizations of complex flow dynamics in the subsurface. Still, it is unsuitable for production or uncertainty quantification due to its prohibitive computational complexity. Balanced truncation (BT) is a well-known model reduction technique for linear systems. It is input-output invariant and does not require a training phase once the system can be written in a linear state-space form, unlike other methods (Proper Orthogonal Decomposition - POD, Deep learning, among others). However, reduced-order models are unsuitable for long-term simulations as these simulations exhibit highly nonlinear behavior. This paper builds upon the bilinear formulation of dynamical systems to construct a suitable reduced-order model. A combination of data-driven model reduction strategies and machine learning (deep-neural networks ANN) will be used to simultaneously predict state and the best correlated input-output matching. We remove the states that are hard to control and observe in the bilinear space by introducing a loss function to the Artificial Neural Network (ANN) training process based on the variational interpretation of the controllability and observability gramians. Both these matrices are related respectively to the energy demanded to control a state (i.e., how hard is it to change a gridblock pressure by controlling the injector wellťs bottom-hole pressure) and to the energy produced by a state (i.e., if we can infer the pressure in a gridblock by measuring the rate of a producing well). We applied this new framework to a two-dimensional two-phase (oil and water) reservoir under waterflooding with three wells (one injector and two producers). The proposed method is a non-intrusive data-driven method as it does not need access to the reservoir simulation's internal structure; thus, it can be easily applied with any commercial reservoir simulator and is extensible to other studies. Although state information is well preserved during truncation, the output, e.g., cumulative production, presents a slightly worse response than simply applying POD. This is because we also identify the output matrices (C and D) and enforce the orthogonalization of the projection matrices through a loss function and not by construction. As far as we know, it is the first attempt to apply balanced truncation of bilinear reservoir models to solve well-control problems. It has the potential to lead the trend of generating robust reduced-order (proxy) models. In this paper, we propose a novel data-driven framework to construct the proxy model while using as much physical information as possible to guide the neural network to best correlates the input well-control and output well-response, making it ideal for well-control optimization.